YOLOv5改进——加入解耦合头

YOLOv5s+解耦合头（Decoupled Head）

最近，在看YOLOv5s的网络结构，看了一段时间感觉还可以，就想着在看看能不能改进一下。现在网上对于YOLOv5的改进可谓是应有尽有，在这里，写一下关于自己加入解耦合头的改进。实验之后，发现map值提升了1.8（基于我自己的数据集），还是不错的‍

第一步

在common.py文件下，加入以下代码，直接复制即可！

#加入解耦头！！！！！！！！！！！！！！！！！
class DecoupledHead(nn.Module):
    def __init__(self, ch=256, nc=80,  anchors=()):
        super().__init__()
        self.nc = nc  # number of classes
        self.nl = len(anchors)  # number of detection layers
        self.na = len(anchors[0]) // 2  # number of anchors
        self.merge = Conv(ch, 256 , 1, 1)
        self.cls_convs1 = Conv(256 , 256 , 3, 1, 1)
        self.cls_convs2 = Conv(256 , 256 , 3, 1, 1)
        self.reg_convs1 = Conv(256 , 256 , 3, 1, 1)
        self.reg_convs2 = Conv(256 , 256 , 3, 1, 1)
        self.cls_preds = nn.Conv2d(256 , self.nc * self.na, 1)
        self.reg_preds = nn.Conv2d(256 , 4 * self.na, 1)
        self.obj_preds = nn.Conv2d(256 , 1 * self.na, 1)
 
    def forward(self, x):
        x = self.merge(x)
        x1 = self.cls_convs1(x)
        x1 = self.cls_convs2(x1)
        x1 = self.cls_preds(x1)
        x2 = self.reg_convs1(x)
        x2 = self.reg_convs2(x2)
        x21 = self.reg_preds(x2)
        x22 = self.obj_preds(x2)
        out = torch.cat([x21, x22, x1], 1)
        return out

第二步

在yolo.py文件下，加入以下代码，直接复制即可！

#加入解耦头！！！！！！！！！！！！！！！！！！！！
class Decoupled_Detect(nn.Module):
    stride = None  # strides computed during build
    onnx_dynamic = False  # ONNX export parameter
    export = False  # export mode
 
    def __init__(self, nc=6, anchors=(), ch=(), inplace=True):  # detection layer
        super().__init__()
 
        self.nc = nc  # number of classes
        self.no = nc + 5  # number of outputs per anchor
        self.nl = len(anchors)  # number of detection layers
        self.na = len(anchors[0]) // 2  # number of anchors
        self.grid = [torch.zeros(1)] * self.nl  # init grid
        self.anchor_grid = [torch.zeros(1)] * self.nl  # init anchor grid
        self.register_buffer('anchors', torch.tensor(anchors).float().view(self.nl, -1, 2))  # shape(nl,na,2)
        self.m = nn.ModuleList(DecoupledHead(x, nc, anchors) for x in ch)
        self.inplace = inplace  # use in-place ops (e.g. slice assignment)
 
    def forward(self, x):
        z = []  # inference output
        for i in range(self.nl):
            x[i] = self.m[i](x[i])  # conv
            bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
            x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
 
            if not self.training:  # inference
                if self.onnx_dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]:
                    self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i)
 
                y = x[i].sigmoid()
                if self.inplace:
                    y[..., 0:2] = (y[..., 0:2] * 2 + self.grid[i]) * self.stride[i]  # xy
                    y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
                else:  # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953
                    xy, wh, conf = y.split((2, 2, self.nc + 1), 4)  # y.tensor_split((2, 4, 5), 4)  # torch 1.8.0
                    xy = (xy * 2 + self.grid[i]) * self.stride[i]  # xy
                    wh = (wh * 2) ** 2 * self.anchor_grid[i]  # wh
                    y = torch.cat((xy, wh, conf), 4)
                z.append(y.view(bs, -1, self.no))
 
        return x if self.training else (torch.cat(z, 1),) if self.export else (torch.cat(z, 1), x)
 
    def _make_grid(self, nx=20, ny=20, i=0, torch_1_10=check_version(torch.__version__, '1.10.0')):
        d = self.anchors[i].device
        t = self.anchors[i].dtype
        shape = 1, self.na, ny, nx, 2  # grid shape
        y, x = torch.arange(ny, device=d, dtype=t), torch.arange(nx, device=d, dtype=t)
        yv, xv = torch.meshgrid(y, x, indexing='ij') if torch_1_10 else torch.meshgrid(y, x)  # torch>=0.7 compatibility
        grid = torch.stack((xv, yv), 2).expand(shape) - 0.5  # add grid offset, i.e. y = 2.0 * x - 0.5
        anchor_grid = (self.anchors[i] * self.stride[i]).view((1, self.na, 1, 1, 2)).expand(shape)
        return grid, anchor_grid

第三步

在yolo.py文件下，找到parse_model函数，在下图地方加入改动部分，以及其他一些小改动。

第四步

在yolov5s.yaml（我自己的是yolov5s，你们根据自己的模型进行更换yaml文件）文件中，进行最后一步改动，在head中把Detect改为Decoupled_Detect，其他不变。

总结

至此，关于yolov5加入解耦合头的改进完成，总的来说 提升性能还是不错的，就是会增大参数量

参考

参考了以下文章:
YOLOv5 Head解耦